Genetics, Evolution and Radiation
Bibliographic record
Abstract
This book is dedicated to the great scientist and outstanding individual Nikolay Wladimirovich Timofeeff-Ressovsky. The book brings together a number of brief stories/essays about Timofeeff-Ressovsky including “Stories told by himself”, and scientific chapters addressing his major research areas: genetics, radiobiology, radiation ecology and epidemiology, and evolution. Timofeeff-Ressovsky contributed to several fields of biology and established new directions of scientific research. He often repeated the phrase, which would later become famous: “Science should not be approached with the ferocity of wild animals”. In keeping with that philosophy, the issues discussed here are still open. Each scientific part starts with a current review; the chapters present leading scientific schools and views. The main theme discussed in the genetics part is mutation variability in the context of linear (replication, transcription, translation) and conformational template processes, and its dependence on phylogenetic group. In turn, the radiobiology chapters focus on the reorganization of DNA, cell, and population variability under low-dose irradiation, sparking indirect processes and adaptive response. The radiation ecology and epidemiology parts present data on the consequences of nuclear plants and related accidents for ecological systems and human beings. Here some approaches to estimating radiation risks are also offered. Evolution laws are demonstrated in the genomic universe, plant-microbe symbiosis, stabilizing and destabilizing (directional) selection. The last essay demonstrates the principles of organization operating in local animal populations, which are approached as social organisms of complex systemic nature. The chapter 'Radiation-Induced Aging and Genetic Instability of Mesenchymal Stem Cells: An Issue for Late Health Effects?' is available open access under a CC BY 4.0 license.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".